1. Field of the Invention
The present invention relates to an image retrieval apparatus, an image retrieval method, and a computer storage medium.
2. Description of the Related Art
There have been discussed many techniques for retrieving similar images. First, there is a method for retrieving a similar image using an overall feature amount of images. For example, Japanese Patent Application Laid-Open No. 8-249349 discusses a method for retrieving a similar image based on color position information by dividing an image into a plurality of blocks and performing pattern matching based on a representative color of each block.
Second, a method for retrieving a similar image using local feature amounts of images has been discussed. According to these methods, first, feature points (local feature points) are extracted from the images. Then, feature amounts (local feature amounts) are calculated for the local feature points based on the local feature points and image information of their vicinities. Image retrieval is performed by matching local feature amounts with one another.
As regards the above method using the local feature amount, a method has been discussed which can retrieve an image even when then image is rotated, enlarged, or reduced by defining the local feature amount as an amount including a plurality of elements invariant in rotation, enlargement, or reduction (C. Schmid and R. Mohr, “Local gray value invariants for image retrieval,” IEEE Trans. PAMI., Vol. 19, No. 5, pp. 530 to 534, 1997).
Many retrieval systems based on local feature amounts perform filter processing such as blurring processing for input images so as to give noise immunity to the image. Generally, the filter processing becomes convolution processing between a filter window and an image. In this case, the filter window protrudes from the image at an edge portion thereof, so that accurate filter processing cannot be performed.
In the edge portion of the image, calculation of a local feature amount is difficult. In calculation of a local feature amount, first, an area (local feature amount calculation area) of a small size around a local feature point is set. Then, a local feature amount is calculated based on a pixel pattern in the local feature amount calculation area. As in the above case, in the edge portion of the image, the local feature amount calculation area protrudes from the image.
Thus, in the retrieval system based on the local feature amount, there are restrictions on calculation of the local feature amount. However, in the conventional retrieval system, consideration to users who have only limited knowledge about the restrictions has been inadequate, and no method has been provided to designate retrieval conditions accurately considering areas where local feature amounts can be calculated. Thus, if a user is unaware that a retrieval area selected as a query is out of an area where a local feature amount can be accurately calculated, the user cannot acquire any retrieval result as intended.